Statistics in Python

This is a brief overview of statistics in python. In data science we always inspect our data using descriptive statistics and descriptive plots. Such statistics can be used of course for visualisations or dashboards as well. The statistical analysis can be done with a number of tests, depending on the characteristics of the data and the research question to be answered. Statistical analysis consists of three parts

• Practical: Descriptive statistics

• Graphical: Descriptive plots

• Analytical: Statistical analysis

Descriptive statistics

Let us create some data for demonstration purpose. We will put the data in a pandas dataframe since pandas has some nice numpy methods built ins, like mean(), sum(), max(),min() etc. It can even deliver the descriptive statistics at once with describe()

#series of values with weights
x = [8.0, 1, 2.5, 4, 28.0]
w = [0.1, 0.2, 0.3, 0.25, 0.15]
X = pd.DataFrame({'measurement':x, 'weights':w})

print((1 + 2.5 + 4 + 8 + 28) / 5 )
print(X.measurement.mean())

8.7
8.7

print((1 + 2.5 + 4 + 8 + 28))
print(X.measurement.sum())

43.5
43.5

X.measurement.min()

1.0

X.measurement.max()

28.0

X.measurement.std()

11.099549540409285

X.measurement.median()

4.0

#weighted average
print((8 * 0.1 + 1 * 0.2 + 2.5 * 0.3 + 4 * 0.25 + 28 * 0.15))
print(np.average(X['measurement'], weights=X['weights']))

6.95
6.95

Most of the time you just want a nice table with the descriptives

X.describe()

	measurement	weights
count	5.00000	5.000000
mean	8.70000	0.200000
std	11.09955	0.079057
min	1.00000	0.100000
25%	2.50000	0.150000
50%	4.00000	0.200000
75%	8.00000	0.250000
max	28.00000	0.300000

Which you can modify like every other dataframe

nice_table = X.describe().T.round(2)
nice_table[['count', 'mean', 'std']]

	count	mean	std
measurement	5.0	8.7	11.10
weights	5.0	0.2	0.08

Descriptive plots

We can also use the built in plots for our exploratory data analyses. Like boxplot(), hist(), plot.kde() or just plot()

# boxplot for outlier check
X.boxplot()

boxplot of X

#check distribution of data
X['measurement'].hist()

histogram of X measurement

#check distribution for normality
X['measurement'].plot.kde()

density plot of X measurement

#check normality with qqplot
from statsmodels.graphics.gofplots import qqplot
from matplotlib import pyplot
qqplot(X['measurement'], line='s')
pyplot.show()

qqplot of X measurement

#plot to see time effect
X['measurement'].plot()

line plot of X measurement

Analytical statistics

Normality check with Shapiro-Wilk Test

It is good practice to check for normality. The Shapiro-Wilk Test is a good test for checking normality

from scipy.stats import shapiro
# normality test
stat, p = shapiro(X['measurement'])
print('Statistics=%.3f, p=%.3f' % (stat, p))
# interpret
alpha = 0.05
if p > alpha:
    print('Sample looks Gaussian (fail to reject H0)')
else:
    print('Sample does not look Gaussian (reject H0)')

Statistics=0.754, p=0.032
Sample does not look Gaussian (reject H0)

source: https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/

More statistics

There are a number of cheatsheets and tutorials on the internet. The next overview is a compact overview of tutorials

• https://www.kaggle.com/hamelg/python-for-data-21-descriptive-statistics
• https://www.kaggle.com/hamelg/python-for-data-22-probability-distributions
• https://www.kaggle.com/hamelg/python-for-data-23-confidence-intervals
• https://www.kaggle.com/hamelg/python-for-data-24-hypothesis-testing
• https://www.kaggle.com/hamelg/python-for-data-25-chi-squared-tests
• https://www.kaggle.com/hamelg/python-for-data-26-anova/notebook
• https://www.kaggle.com/hamelg/python-for-data-27-linear-regression